We build a large and heterogeneous graph to connect the sentences sharing the properties: entity types and textual context, which are essential for RE [1]}, [2]}, [3]}.
The heterogeneous graph is defined as \(G = (\mathcal {V}, \mathcal {E})\) , where \(\mathcal {V}\)  is the set of nodes, and \(\mathcal {E}\)  is the set of edges.
\(\mathcal {V} = \mathcal {V}_S \cup \mathcal {V}_P\) , where \(\mathcal {V}_S\)  is the set of sentences, and \(\mathcal {V}_P = \mathcal {V}_C \cup \mathcal {V}_E\)  is the property caches.
Here \(\mathcal {V}_C\)  is the set of latent topics [4]}
mined from the latent topics from the text corpus using LDA [5]}, which has been found effective in modeling useful contextual patterns [6]}.
Each topic is represented by a probability distribution over the words, and we assign each sentence to the top \(P\)  topics with the largest probabilities.
\(\mathcal {V}_E\)  is the set of entity types, where every cache represents the types of an entity pair.
The entity types are also crucial for predicting relations [1]}, [3]}.
An edge \((p,s)\in \mathcal {E}\)  exists if the sentence \(s\in \mathcal {V}_S\)  has the property \(p\in \mathcal {V}_P\) .
